Stanislas0 / KDD_CUP_2020_MLTrack2_SPEITLinks
KDD CUP 2020 ML Track 2 "Adversarial Attacks and Defense on Academic Graph": 1st Place Solution
☆20Updated 4 years ago
Alternatives and similar repositories for KDD_CUP_2020_MLTrack2_SPEIT
Users that are interested in KDD_CUP_2020_MLTrack2_SPEIT are comparing it to the libraries listed below
Sorting:
- This repository contains the official implementation of the paper "Robustness of Graph Neural Networks at Scale" (NeurIPS, 2021).☆30Updated 2 years ago
- Graph Robustness Benchmark: A scalable, unified, modular, and reproducible benchmark for evaluating the adversarial robustness of Graph M…☆95Updated last year
- Adversarial training for Graph Neural Networks☆60Updated 4 years ago
- [ICLR 2022] Understanding and Improving Graph Injection Attack by Promoting Unnoticeability☆37Updated last year
- Adversarial Attack on Graph Structured Data (https://arxiv.org/abs/1806.02371)☆129Updated 3 years ago
- [NeurIPS '21] Adversarial Attacks on Graph Classification via Bayesian Optimisation (GRABNEL)☆13Updated 3 years ago
- Adversarial attacks and defenses on Graph Neural Networks.☆384Updated last year
- Implementation of the paper "Adversarial Attacks on Neural Networks for Graph Data".☆221Updated 3 years ago
- ☆57Updated 2 years ago
- Code for Towards More Practical Adversarial Attacks on Graph Neural Networks (NeurIPS 2020)☆28Updated 3 years ago
- Implementation of the paper "Adversarial Attacks on Graph Neural Networks via Meta Learning".☆150Updated 3 years ago
- Defending graph neural networks against adversarial attacks (NeurIPS 2020)☆71Updated 2 years ago
- A Restricted Black-box Adversarial Framework Towards Attacking Graph Embedding Models☆35Updated 4 years ago
- This repo keeps track of popular provable training and verification approaches towards robust neural networks, including leaderboards on …☆98Updated 2 years ago
- A curated collection of adversarial attack and defense on graph data.☆570Updated last year
- Adaptive evaluation reveals that most examined adversarial defenses for GNNs show no or only marginal improvement in robustness. (NeurIPS…☆29Updated 2 years ago
- Graph Injection Adversarial Attack & Defense Dataset , extracted from KDD CUP 2020 ML2 Track☆22Updated 11 months ago
- G-NIA model from "Single Node Injection Attack against Graph Neural Networks" (CIKM 2021)☆29Updated 3 years ago
- A PyTorch implementation of "Backdoor Attacks to Graph Neural Networks" (SACMAT'21)☆40Updated 3 years ago
- ☆32Updated 3 years ago
- Locally Private Graph Neural Networks (ACM CCS 2021)☆48Updated last month
- ☆2Updated 4 years ago
- Official implementation of our FLAG paper (CVPR2022)☆145Updated 3 years ago
- code for paper TDGIA:Effective Injection Attacks on Graph Neural Networks (KDD 2021, research track)☆20Updated 3 years ago
- Pytorch implementation of gnn meta attack (mettack). Paper title: Adversarial Attacks on Graph Neural Networks via Meta Learning.☆21Updated 4 years ago
- A united toolbox for running major robustness verification approaches for DNNs. [S&P 2023]☆90Updated 2 years ago
- ☆28Updated 2 years ago
- ☆10Updated 4 years ago
- A curated list of adversarial attacks and defenses papers on graph-structured data.☆860Updated last year
- [ICML 2021] "A Unified Lottery Tickets Hypothesis for Graph Neural Networks", Tianlong Chen*, Yongduo Sui*, Xuxi Chen, Aston Zhang, Zhang…☆66Updated last year